Author
Yüksel, Ulaş, Sözer, Hasan
Publication Date
2013
Publication Place
-
IEEE
Subject
Alert classification, Industrial case study, Static code analysis
Type
belge
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1063-6773
Record ID
38548e7a-b4ed-433e-b0af-cc8779f5373b
Library Location
Computer Science
Date
2013
Notes
Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text
Static code analysis tools automatically generate alerts for potential software faults that can lead to failures. However, developers are usually exposed to a large number of alerts. Moreover, some of these alerts are subject to false positives and there is a lack of resources to inspect all the alerts manually. To address this problem, numerous approaches have been proposed for automatically ranking or classifying the alerts based on their likelihood of reporting a critical fault. One of the promising approaches is the application of machine learning techniques to classify alerts based on a set of artifact characteristics. In this work, we evaluate this approach in the context of an industrial case study to classify the alerts generated for a digital TV software. First, we created a benchmark based on this code base by manually analyzing thousands of alerts. Then, we evaluated 34 machine learning algorithms using 10 different artifact characteristics and identified characteristics that have a significant impact. We obtained promising results with respect to the precision of classification.
DOI
10.1109/ICSM.2013.89
